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dstack on CUDO Compute

dstack is an open-source alternative to Kubernetes and Slurm, built to streamline AI development and deployment across cloud providers. dstack provides the following capabilities:
  • Dev Environments: Quickly set up remote machines with your code and IDE for interactive coding. Ideal for testing code in your IDE or notebook before task scheduling or deployment.
  • Tasks: Schedule jobs or run web apps with configurable dependencies, resources, and more. Suited for training, fine-tuning, or app testing, with support for distributed clusters.
  • Services: Deploy scalable web apps or models as private or public endpoints, complete with dependency management, resource allocation, and scaling rules.
  • Fleets: Efficiently manage clusters and instances for cloud and on-prem resources, reusable across dev environments, tasks, and services.
dstack is available as a self-hosted open-source version and a hosted marketplace version.

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Install dstack

Create a config file for CUDO and save it to ~/.dstack/server/config.yml. You will need to have created a project and know its id, and you will need to create an API key in the CUDO Compute web console. 
projects:
  - name: main
    backends:
      - type: cudo
        project_id: my-cudo-project
        creds:
          type: api_key
          api_key: 7487240a466624b48de22865589
Install dstack and start the server 
pip install "dstack[all]" -U
dstack server

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Try out a dstack deployment

Now you can create a project directory and initialize it for use with dstack: 
mkdir quickstart && cd quickstart
dstack init
Create a yaml file .dstack.yaml inside your project directory to define your dstack deployment, here we are deploying a remote IDE (vscode): 
type: dev-environment
# The name is optional, if not specified, generated randomly
name: vscode

python: '3.11'
# Uncomment to use a custom Docker image
# image: dstackai/base:py3.13-0.6-cuda-12.1

ide: vscode

# Use either spot or on-demand instances
spot_policy: auto

# Uncomment to request resources
# resources:
#  gpu: 24GB
Now apply the dstack configuration: 
dstack apply -f .dstack.yml

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More information

Please check the dstack documentation for the most up-to-date features.